Treatment outcome prediction for lung volume reduction procedures

ABSTRACT

Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.

CROSS-REFERENCES

This application is a continuation of U.S. application Ser. No.15/362,343, filed Nov. 28, 2016, which is a continuation of U.S.application Ser. No. 14/188,005, filed Feb. 24, 2014, which issued asU.S. Pat. No. 9,504,529 on Nov. 29, 2016. The entire content of theseapplications are hereby incorporated by reference in their respectiveentirety.

BACKGROUND OF THE INVENTION

Severe emphysema is a debilitating disease that limits quality of lifeof patients and represents an end state of Chronic Obstructive PulmonaryDisease (COPD). It is believed that 3.5 million people in the US havethe severe emphysematous form of COPD, and it is increasing in bothprevalence and mortality. Current treatment methods for severe emphysemainclude lung volume reduction (LVR) surgery, which is highly invasive,and can be risky and uncomfortable for the patient. New treatmentmethods for treating emphysema include bronchoscopy guided lung volumereduction devices that aim to close off ventilation to the diseasedregions of the lung, but maintain ventilation to healthier lung.Bronchoscopy-guided techniques have the promise to be less invasive,less costly and more highly accurate treatments for patients with severedisease and improve the quality of life of severe emphysema patients.

Emphysema can present itself in various disease forms (i.e.,phenotypes). Predicting the right treatment for these patients at theappropriate time in the disease process may depend on the phenotype ofthe disease. Imaging techniques provide an in-vivo mechanism toobjectively quantify and characterize disease phenotype and can be usedas the patient selection process for the various procedural options.Quantitative imaging biomarkers can be used to effectively phenotypedisease and therefore predict those patients most likely to respond tothe targeted treatment options. By triaging the right patient to theappropriate therapy, there exists a greater promise for a positiveimpact on patient outcome, reduced healthcare costs, and replacing moreinvasive procedures like LVR surgery in treating patients with severeemphysema.

Bronchoscopic procedures such as the placement of pulmonary valves,coils, and the use of bio-sealants and energy delivery for lung volumereduction can provide effective ways of treating emphysema by shrinkingover-inflated portions of the lungs. However, because of the complexityof lung anatomy and the diversity of disease among individuals, planningfor such procedures can be difficult. For example, it can be difficultto determine which locations are best suited for the placement of valvesand whether how such locations can be best accessed bronchoscopically.Difficulties can therefore arise after such a treatment is already inprogress, such as difficulties in accessing the location for placementof the valve or delivery of the bio-sealant or energy, or the results ofsuch treatment may be less effective than anticipated due to diseaseaspects that might not have been appreciated before the procedures suchas fissure integrity and the presence of collateral ventilation.

Another lung disease, lung cancer, is the world's leading cause ofcancer death, causing more annual deaths (about 28% of all cancerdeaths) than any other cancers for which there are routing screeningprograms such as breast, colorectal, and prostate. Lung cancer comprisesabout 14% of cancer diagnoses each year, including smokers as well asnon-smokers. Only about 15% of lung cancer cases are diagnosed at anearly stage while 85% are diagnoses at a late stage. As with allcancers, early detection of lung cancer is critical to patient outcome.However, the five-year survival rate for lung cancer is only about 16%,and over half of patients die within the first year of diagnosis. Thefive-year survival rate for lung cancer is much lower than that of manyother leading cancers, but this could be improved through improved earlydetection.

One method of screening for lung cancer uses low-dose computedtomography (CT), which resulted in a 20% reduction in lung cancermortality in one trail. The U.S. Preventive Services Task Force hasrecommended annual screening for lung cancer using low-dose CT foradults aged 55 to 80 years old who have a 30 pack-year smoking historyand currently smoke or have quit within the past 15 years, due to theincreased risk for lung cancer in this population. However, severalpublications have shown an association between radiographic emphysemaand airflow obstruction and lung cancer, confirming the presence ofemphysema or airflow obstruction in most middle-aged to older long-termsmokers and ex-smokers with proven lung cancer. Therefore, while thecriteria currently employed for selecting patients for low-dose CT lungcancer screening are useful, other factors may also be useful and a morerefined selection process could make lung cancer screening more costeffective.

SUMMARY

Certain embodiments of the present invention are described in thefollowing illustrative embodiments. Various embodiments include systemsand methods for planning lung procedures such as lung volume reductionprocedures using predictions based on volumetric patient lung images.Other various embodiments include systems and methods for predictinglung cancer risk and for recommending screening regimens for patientsusing predictions based on volumetric images of the patient's lungs.

In some embodiment, a system for displaying a predicted outcome of alung volume reduction procedure for a patient includes a user interface,a processor, and programing operable on the processor for displaying apredicted outcome of the bronchoscopic lung volume reduction procedureon the user interface. Displaying the predicted outcome of the lungvolume reduction procedure may include receiving patient data includingvolumetric images of the patient, analyzing the volumetric images toidentify one or more features correlated to treatment outcomeprediction, predicting an outcome for a treatment modality or treatmentdevice using the one or more identified features, and displaying thepredicted outcome on the user interface.

The system may further include receiving a selected treatment locationwithin the airway tree from a user and predicting an outcome for atreatment modality or treatment device at the selected location usingthe one or more identified features. In some embodiments, displaying thepredicted outcome of the lung volume reduction procedure furtherincludes receiving a selected treatment modality from the user, andpredicting an outcome for a treatment modality includes predicting anoutcome for the treatment modality selected by the user.

In some embodiments, predicting an outcome for a treatment modalityincludes predicting a plurality of outcomes for a plurality of treatmentmodalities, and displaying the predicted outcome on the user interfaceincludes displaying the plurality of treatment outcomes for theplurality of treatment modalities on the user interface.

In some embodiments, the system further includes programming operable onthe processor for analyzing the volumetric images to identify lobes,sublobes and an airway tree of the lungs and displaying a threedimensional model of the patient's lungs on the display.

In some embodiments, the predicted outcome may be a numerical valuerepresenting a probability of success. In some such embodiments, successmay be a lung volume reduction greater than a threshold value. In someembodiments, the predicted outcome includes a numerical valuerepresenting a probability of pneumothorax. In some embodiments, thepredicted outcome includes a first numerical value representing aprobability of lung volume reduction greater than a threshold value anda second numerical value representing a probability of pneumothorax.

In some embodiments, analyzing the volumetric images to identify one ormore features correlated to treatment outcome prediction comprisesmeasuring low attenuation clusters. In some embodiments, the one or morefeatures include a feature corresponding to fissure integrity. In someembodiments, analyzing the volumetric images to identify one or morefeatures correlated to treatment outcome prediction comprises measuringperipheral vessel volume.

Other embodiments include a system for displaying an outcome of a lungvolume reduction procedure for a patient including a user interface, aprocessor, and programming operable on the processor for displaying apredicted outcome of the bronchoscopic lung volume reduction procedureon a user interface. Displaying the predicted outcome of the lung volumereduction procedure includes receiving patient data comprisingvolumetric images of the patient, analyzing the volumetric images toidentify lobes and airway tree of the lungs, analyzing the volumetricimages to identify one or more features correlated to treatment outcomeprediction, displaying a three dimensional model of the patient's lungs,receiving a selected treatment location within the airway tree from auser, predicting an outcome for a treatment modality or treatment deviceat the selected location using the one or more identified features, anddisplaying the predicted outcome on the user interface. Displaying thepredicted outcome of the lung volume reduction procedure may furtherinclude receiving a selected treatment modality or treatment device fromthe user, and predicting an outcome for a treatment modality may includepredicting an outcome for the treatment modality or treatment deviceselected by the user. In some embodiments, displaying the predictedoutcome of the lung volume reduction procedure further includesdisplaying a probability of a successful treatment outcome and aprobability of an adverse event for a plurality of lung volume reductiontreatment modalities and receiving a selection of a treatment modalityfrom a user.

In some embodiments, predicting an outcome for a treatment modality ortreatment device includes predicting a plurality of outcomes for aplurality of treatment modalities or treatment devices, and displayingthe predicted outcome on the user interface includes displaying theplurality of treatment outcomes for the plurality of treatmentmodalities on the user interface.

In some embodiments, the predicted outcome includes a numerical valuerepresenting a probability, such as a probability of success.

In some embodiments, predicting an outcome for the selected treatmentmodality using one or more identified features includes comparing theone or more features identified in the patient lungs to a database topredict an outcome of treatment at the selected location with theselected treatment modality. The database may include a set of outcomesfor lung volume reduction procedures for a group of individuals usingthe selected treatment modality and further may include a set ofvolumetric images or one or more features identified in the volumetricimages for the group of individuals, and the identified features in thevolumetric images of the group of individuals may be the same featuresas the identified features in the volumetric images of the patient.

Other embodiments include a method of planning a lung volume reductionprocedure for a patient using a treatment planning and outcome system,the system comprising a processor and a user interface. The methodincludes observing a three dimensional model of the patient's lungs onthe user interface generated by the processor using patient datacomprising volumetric images, selecting a treatment modality, selectinga treatment location, and observing a predicted outcome on the userinterface. The predicted outcome may be generated by the using outcomepredictors determined using the patient data. The method may furtherinclude observing a predicted outcome for a plurality of treatmentmodalities generated by the processor and displayed on the userinterface prior to selecting a treatment modality.

Still other embodiments include systems for selecting patients for lungcancer screening comprising. In some such embodiments, the systemincludes a user interface, a processor, and programing operable on theprocessor for displaying a lung cancer risk or a recommendation for lungcancer screening. Displaying the lung cancer risk or the recommendationfor lung cancer screening includes receiving patient data comprisingvolumetric images of the patient, analyzing the volumetric images toidentify one or more features correlated to lung cancer, predicting alikelihood of lung cancer using the one or more identified features, anddisplaying the predicted likelihood of lung cancer on the user interfaceor displaying the recommendation for lung cancer screening. Therecommendation for lung cancer screening may be determined by the systemusing the predicted likelihood of lung cancer.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are illustrative of particular embodiments of theinvention and therefore do not limit the scope of the invention. Thedrawings are not necessarily to scale (unless so stated) and areintended for use with the explanations in the following detaileddescription.

Embodiments of the invention will hereinafter be described with theappended drawings, wherein like numerals denote like elements.

FIG. 1 is an example of a schema for prediction of an outcome of a lungvolume reduction procedure.

FIG. 2 is an example of outcome prediction for various treatmentmodalities and identification of a preferred treatment modality.

FIG. 3 is an example of a method of treatment planning and outcomeprediction.

FIG. 4 is an example of a user interface for treatment planning andoutcome prediction.

FIG. 5 is an example of a user interface for displaying a treatment planincluding outcome prediction.

FIG. 6 is another example of a schema for prediction of an outcome of alung volume reduction procedure.

FIG. 7 is another example of a method of treatment planning and outcomeprediction.

FIG. 8 is an example of a user interface for selecting lung cancer riskassessment and treatment outcome prediction.

FIG. 9 is an example of a user interface for treatment planning andoutcome prediction.

FIG. 10 is an example of a user interface for treatment planning andoutcome prediction.

FIG. 11 is an example of a user interface for treatment planning andoutcome prediction for valve placement.

FIG. 12 is an example of a user interface for treatment planning andoutcome prediction for coil placement.

FIG. 13 is an example of a method of lung cancer risk assessment.

FIG. 14 is an example of a user interface for selecting lung cancer riskassessment and treatment outcome prediction.

FIG. 15 is an example of a user interface for lung cancer riskassessment.

FIG. 16 is a graph of wall thickness verses the probability of valveplacement errors.

FIG. 17 is a graph of wall area fraction versus the probability of valveplacement error.

FIG. 18 is a graph of tapering versus the probability of valve placementerror.

FIG. 19 is a graph of the probability of pneumothorax versus theprobability of successful lung volume reduction.

DETAILED DESCRIPTION

Embodiments described herein include systems for predicting variousaspects of lung diseases, including form, stage, or severity. Someembodiments predict the outcome for one or more procedures, such as theoutcome of a lung volume reduction procedure for the pulmonary diseasessuch as emphysema. Because of the complexity of the lungs and anatomicaldifferences amongst individuals, a wide variety of factors can influencedisease form and severity and the success of interventional pulmonaryprocedures. Various embodiments can identify predictors and can predictthe outcome of procedures such as interventional pulmonary proceduresand may be used as a part of treatment planning for interventionalpulmonary treatments such as lung volume reduction treatments, includingvalve placement, the use of bioadhesives and energy modalities. Thetreatment outcome prediction system may provide a clinician withenhanced visualization and analysis of the lungs including assisting theclinician with triaging patients and with planning a procedure with thehighest likelihood of a successful outcome.

Various embodiments predict the outcome of an interventional pulmonaryprocedure using measurements obtained from patient images and/or otherpatient data. The patient images may be patient images or imaging dataproduced by CT scans, MM scans, and/or PET scans or other volumetricimages, for example. The measurements may be predictors that may be usedindividually or in combination to identify a person's disease form,stage, and/or severity and/or to predict a person's outcome from aprocedure. Measurements which may be used as predictors includequantitative measurements of the lung parenchyma and/or airways. Forexample, density, texture, airway morphology (such as diameter,cross-sectional area, length, tapering, wall thickness, and/or wall areaof a particular airway), pulmonary vessels (such as size, size relativeto neighboring airway, volume of peripheral vessels relative to totalvolume of pulmonary vasculature), fissure characterization, orfunctional measurements such as measurements of air trapping (determinedeither from the patient images or from a separate measurement). Fissurecharacterization may be determined as described in U.S. patentapplication Ser. No. 13/804,542, for example, the disclosure of which isincorporated herein by reference.

In some embodiments, the predictors may be identified using a trainingdatabase. The training database may include information from a largegroup of individuals. The information may include the patients' personalcharacteristics such as age, gender, race, body mass index, height,and/or lung volume, among other things which may be consideredconfounding variables and which may be used to categorize the patientswithin the database for refining the information contained in thedatabase. The information in the database may also include imaging dataincluding the imaging data itself and/or data obtained by analysis ofthe images or imaging data, such as measurement of various anatomicalfeatures determined using the imaging data, such as the predictorsdescribed above. The information may further include informationrelating to patient treatment and outcome, including type of treatmentsuch as type of lung volume reduction procedure, location of treatmentwithin the lung, the results of treatment, pulmonary function tests,amount of lung volume reduction, performance test results such as 6minute walk distance, and occurrence of adverse events following theprocedure, such as pneumothorax or death. The type of lung volumereduction procedure may include valve placement, energy delivery, orbiosealant, for example. The device information may further include thetype of device, the type of valve (such as endobronchial orintrabronchial) and the specific manufacturer and/or model of thedevice. The information in the database may further include informationfrom other studies besides the volumetric images, such as results ofdirect measurements such as testing for collateral ventilation, whichmay be performed using the Chartis System from Pulmonx, for example. Theinformation may further include results from pulmonary function studies(such as FEV1, FEV1/FVC, and FEF) and patient questionnaires (such asthe Saint George's Respiratory Questionnaire (SGRQ) and the ModifiedMedical Research Council Dyspnea Scale (mMRC)), for example. Thetraining database is sufficiently large that statistically significantassociations can be identified, such as associations betweenmeasurements of anatomical features and results of lung volume reductionprocedures, which can be used as predictors. In some cases, thepredictors may be identified for each subgroup of individuals, such asindividuals of a particular age, gender, race, body mass index, height,lung volume and/or valve type or procedure type.

Predictors of patient disease state (such as form of disease, stage, orseverity) and/or of treatment outcomes may be derived from the trainingdatabase, such as by extraction from multiple regression analysis. Forexample, correlations between one or more measurements of anatomicalfeatures using patient images and outcomes of lung volume reductionprocedures may be identified in the training database, and themeasurements may be used as a predictor in evaluating patients who arenot included in the training database for clinical decision making. Thepredictors may be identified and used for triaging patients into thosefor whom a procedure is recommended versus those for whom the procedureis not recommended, and may be used for outcome prediction.

The patient predictors may be used singly to predict a disease stateand/or an outcome. Alternatively, two or more predictors may be used incombination to predict a disease state and/or an outcome for a patient.In some embodiments, multiple predictors may be used to create aclassification schema. A patient's data, such as the patient's lungimages, may be analyzed to classify them with regard to the multiplepredictors used in the classification scheme and a prediction of theoutcome may be obtained based on the classification schema.

The outcomes identified using the training database and/or predicted forindividual patients according to various embodiments may include thesuccess of the procedure such as a lung volume reduction procedure. Aprocedure may be classified as successful if certain criteria are met,as discussed further below.

The predictors may be one or more key measurements predictive of atreatment response. The predictors may be used in combination using anexisting classification schema. Alternatively, several types ofsupervised classifiers may be used ranging from simple rule basedclassifiers to tree-based classifiers, Naïve Bayes Classifier, randomforest algorithm, or more sophisticated classifiers which may use acombination of models as described in the machine learning literature,for example. The classification steps may result in an ROC analysis thatindicates the sensitivity, specificity, and positive and negativepredictive value of the test for predicting a result, such as forpredicting a positive outcome for LVR treatment.

Once a predictor or set of predictors has been identified using thetraining database, the predictor or predictors can be applied for usewith patients. As such, the training database may be used to identifypredictors, and then the predictor or predictors can be used by a systemto predict disease state and/or treatment outcome, for example, asdescribed further below. The training database may be updated from timeto time with additional data, and/or additional features may beidentified in the data, after which the predictors may be updated torefine their predictive abilities. Such updated predictors may then betransmitted to the system for predicting patient disease state and/ortreatment outcome.

A positive outcome may be identified by meeting one or more objectivecriteria, which may be measurements, such as measurements which can beobtained from a patient volumetric image such as a CT. For example, apositive treatment response may be identified by a lung volume reductionin the treated lung of at least a selected threshold value, such as 350cubic centimeters. Alternatively, the percentage of lung volumereduction of the treated lobe may be used to determine whether thethreshold value of successful lung volume reduction has been achieved.In another alternative, the increase in the FEV1 after treatment may beused to determine whether the procedure was successful. For example, acut off value of at least a 12% increase in FEV1 may be used asindicating a successful treatment outcome. In some embodiments, animprovement in carbon monoxide diffusing capacity (DLCO) may be used todetermine whether a procedure was successful. In some embodiments, thesubjective improvement in symptoms may be used to determine whether aprocedure was successful. For example, the improvement in the quality oflife may be determined using a standard test such as the St. George'sRespiratory Questionnaire (SGRQ), and a particular numerical orpercentage increase may be used as a threshold to indicate success of aprocedure. The threshold values for any measurement of success may bepreset in the system or may be selected by a clinician using a treatmentplanning and outcome prediction system, for example. In someembodiments, the occurrence of certain adverse event may override anyfindings indicative of success. Such adverse events may include death,pneumothorax, and/or movement of an implanted device for example. Ifsuch adverse events occur, the patient outcome may be categorized as nota success, even if the threshold values are met.

In some embodiments, the system may predict the likelihood of an adverseevent. Adverse events may include the occurrence of pneumothorax, suchas pneumothorax in the treated lung within a threshold period of timefollowing the treatment, or death of the patient within a thresholdperiod of time following the treatment. The thresholds may be selectedsuch that the adverse event is likely to be causally related to theprocedure. Other events that may be considered adverse include movementof an implanted device such as a one way valve, such as movement into achild branch or bifurcation after implantation.

In still other embodiments, the predictors may be used to identify oneor more features of the patient's lung images, which may in turn be usedto predict treatment outcome. One such feature, which may be identifiedin the patient's lung images is the presence of collateral ventilation,which may or may not be used, in turn, to predict treatment outcome. Forexample, one or more anatomical features, which may be identified in thepatient images may be correlated to the presence of collateralventilation using the training database. These anatomical features maythen be used as predictors to predict the presence of collateralventilation in a patient. Furthermore, the presence of collateralventilation as directly measured, such as using the Chartis PulmonaryAssessment System™, or as predicted using the image based predictors,may further be correlated to outcomes, such as successful treatment oroccurrence of an adverse event, and may therefore be used to predictpatient outcomes.

In some embodiments, the predictors identified using the trainingdatabase may be separately determined for distinct patient populationswithin the training database based on one or more personalcharacteristics of the patients (such as age, gender, race, BMI, height,and lung volume). The prediction model applied to a patient may be basedupon a matching patient population within the training database havingthe same personal characteristics. In this way, the confounding effectsof the personal characteristics on outcome prediction can be eliminated,making the patient prediction process more powerful (more sensitive andspecific).

In some embodiments, the development of a predictive model may firstinclude determining a predictive model including one or more predictiveparameters using a large training database of patients and outcomes fora particular treatment, and then applying the predictive model to anindependent test database to predict treatment outcome. The individualsincluded in the training databases may be selected to include a similarpatient population and/or similar procedure type as those in the testpopulation. The test database may then be analyzed to determine whetherthe predictions were correct.

Some embodiments may be used for predicting the outcome of a procedure,such as a lung volume reduction procedure. The lung volume reductionprocedure may be an endoscopic procedure such as placement of a one-wayvalve, coil, biosealant delivery, or heat energy delivery.Alternatively, embodiments may be used to predict other pulmonarytreatment outcomes or aspects of pulmonary disease such as the diseaseform, stage or severity or severity of COPD, emphysema (e.g.,centrilobular, panlobular, paraseptal), asthma, lung cancer, or otherpulmonary diseases. The training database used for identifying suchpredictors would include data identifying these other treatment outcomesor aspects of disease, which may have been determined by conventionalmeans such as radiological studies, physical examination, pathologicalexamination of tissue samples, or other methods.

In some embodiments, various predictors may be used in combination topredict patient status for various conditions or features, and thepredicted conditions or features may be used in combination to predictpatient outcome or disease status. An example of such a combination ofpredictions to predict treatment outcome is shown in FIG. 1. In thisexample, a first model 10 is used to predict collateral ventilation, asecond model 12 is used to predict valve misplacement, which may bebased upon characteristics of the airway into which the valve would beplaced and the nature of the surrounding tissue, a third model 14 isused to predict lung compliance, and a fourth model 16 is used topredict adverse events. In each of these models, the prediction of eachcondition or feature may be made based upon features in the patient'svolumetric images such as CT scans, though other radiologic scans couldalternatively be used. The results of one or more or all four of themodels may then be used to predict the response to treatment 18, such asto lung volume reduction treatment. Each of the models, and the finaltreatment outcome prediction, may be adjusted for patientcharacteristics such as age, gender, race, BMI, height, and lung volume,which could otherwise confound the predictions. One or more of thepredictions made by each of these models and the final treatment outcomeprediction may be made automatically and presented to a clinician toassist with patient selection as part of a treatment planning andprediction system.

The results that are presented to the clinician may be a prediction thatone or more features are either present or absent, such as thatcollateral ventilation is present or absent in a particular lobe orsub-lobe. Alternatively or additionally, the results may be presented asa predicted likelihood, such as a likelihood that a feature is presentor that an outcome will occur, such as a likelihood that collateralventilation is present (such as in an amount above a particularthreshold), which may be presented as a percentage or other numericalvalue representing likelihood.

In some embodiments, the predictors may be used to assist a clinician inselection of a treatment modality. For example, each treatment modality,such as valve placement, coil placement, bio-sealant delivery, energydelivery, stents, etc., may have a different predicted efficacy andsafety (risk of adverse events) associated with its use in a particularpatient. Embodiments of the invention may therefore determine thelikelihood of a successful outcome and/or the likelihood of an adverseevent for a particular patient using one or more predictors, and maypresent this information to a clinician, and may even present apreferred treatment modality. The clinician may use this informationwhen selecting a treatment modality. A representation of such a systemfor device selection is shown in FIG. 2. The probabilities may beseparately determined for each type of treatment modality, in this casevalve placement 20, coil placement 22, and energy delivery (vapor) 24,though other or additional modalities may also be used. The likelihoodof a successful result (indicated as P_eff) and of a severe adverseevents (indicated as P_sae) are determined for each modality and may bedisplayed for a clinician. The valve is selected as the best option at26.

The outcome predictors may be used in conjunction with treatmentplanning, such as in conjunction with, or as a component of, a lungvolume reduction treatment planning system. In such cases, the systemmay be a treatment outcome prediction system. The treatment outcomeprediction system may utilize volumetric images or imaging data toanalyze and identify patient anatomy and may further present 3dimensional models of the patient pulmonary anatomy to a clinician.

The treatment outcome prediction system may include a processor, such asa processor in a computer, and may also include a visual display such asa monitor or other display screen. The system may also includeinstructions included in software, stored in memory of the system, andoperable on the processor. The software may include instructions for theprocessor to perform the various steps and methods described herein,including instructions to receive patient data including volumetricimaging data, analyze the data, display images includingthree-dimensional images of the pulmonary tree, receive physician input,and analyze the pulmonary anatomy in light of the clinician input, andsupply information to the clinician, and suggest treatment locations andapproaches. In some embodiments, the treatment outcome predictionsoftware may be incorporated into 3D pulmonary imaging software. In someembodiments, the treatment outcome prediction software and the 3Dpulmonary imaging software may be separate software but may each beimplemented by and/or incorporated into a common system. An example of3D pulmonary imaging software that may be used in combination with thetreatment planning software is the APOLLO quantitative pulmonary imagingsystem software available from VIDA Diagnostics, Inc.

Embodiments of the invention may allow the clinician to interact withthe three-dimensional model of the lungs and the two-dimensionalvolumetric images associated with the 3-dimensional model. For example,the three-dimensional model and the associated two-dimensional imagesmay be presented in a graphical user interface on a visual display. Theuser may interact with the graphical user interface, such as byselecting a button, icon, and/or one or more locations on the images orthe model or elsewhere using a mouse, stylus, keypad, touchscreen orother type of interface known to those of skill in the art. The creationof the three-dimensional model may be performed by the system includinga processor with software instructions to perform this function as wellas software to permit a user to interact with the graphical userinterface, to calculate and display desired data and images, and toperform the other functions described herein. The system may furtherinclude the visual display on which the graphical user interface isdisplayed. The three-dimensional model and two-dimensional images may beprovided to a user (such as a clinician or researcher) as a graphicaluser interface on a visual display, which may be a computer screen, onwhich the images and data may be manipulated by the user. Outcomepredictions may also be provided on the visual display.

Examples of the embodiments may be implemented using a combination ofhardware, firmware, and/or software. For example, in many cases some orall of the functionality may be implemented in executable softwareinstructions capable of being carried out on a programmable computerprocessor. Likewise, some examples of the invention include acomputer-readable storage device on which such executable softwareinstructions are stored. In certain examples, the system processoritself may contain instructions to perform one or more tasks. Systemprocessing capabilities are not limited to any specific configurationand those skilled in the art will appreciate that the teachings providedherein may be implemented in a number of different manners.

Embodiments which use patient images for prediction of treatment outcomeusing a treatment outcome prediction system will now be described. Thetreatment outcome prediction system may use volumetric patient imagingto provide a platform for a clinician to plan interventional treatmentsfor pulmonary disease and to receive predicted outcomes for the plannedinterventional treatment from the system. One example of the steps of atreatment planning and outcome prediction procedure which may beperformed by the treatment planning and outcome prediction system isshown in the flowchart depicted in FIG. 3. However, it should beunderstood that the steps described herein need not necessarily all beperformed or need not necessarily be performed in the order presentedand various alternatives also exist.

The treatment planning and outcome prediction procedure begins at thestarting step 30 at which a clinician interacts with the system todirect it to begin a new treatment outcome prediction procedure, whichmay be a part of a treatment planning procedure. The clinician mayselect the volumetric patient volumetric images or imaging data to beused for the treatment planning procedure and the system may receive thevolumetric images or imaging data in step 32 as well as other patientdata. The volumetric patient images may be patient images or imagingdata produced by CT scans, MM scans, and/or PET scans, for example, fromwhich a series of two-dimensional planar images (referred to herein astwo-dimensional volumetric images or two-dimensional images) can beproduced in multiple planes, for example. Other patient data which maybe received by the system and which may be useful in the treatmentplanning process includes the patient's emphysema score, lung functiontest results such as FEV1, and collateral ventilation measurements. Forexample, the amount of collateral ventilation may have been determinedby direct measurement using a bronchoscopic system such as the CHARTISSystem. Alternatively, measurements like collateral ventilation may bepredicted at a later step using predictors as described herein.

Next in step 34 the system analyzes the patient data. For example, thesystem may analyze the volumetric images to segment and identify theairways, the lobes, the sublobes, the fissures, and/or other features ofthe lungs. Software for analyzing volumetric images of the lungsincludes 3D imaging software such as the Apollo quantitative pulmonaryimaging software. Methods of identifying and characterizing sublobes aredescribed in U.S. Pat. Pub. No. 2012-0249546, entitled Method and Systemfor Visualization and Analysis of Sublobar Regions of the Lung, which ishereby incorporated by reference. Methods of identifying andcharacterizing the pulmonary fissures are described in U.S. patentapplication Ser. No. 13/804,542, entitled Visualization andCharacterization of Pulmonary Fissures, which is also herebyincorporated by reference. The methods used by the 3D pulmonary imagingsoftware and the U.S. patent applications listed above may be likewiseused to analyze the volumetric images for treatment planning asdescribed herein. Step 34 may include analyzing the patient data forgenerating 3D images. However, the same analysis may also be performedto detect and/or quantify predictors, whether or not a 3D image isactually generated.

After the system has analyzed the patient data, the system may create agraphical user interface in which a 3-dimensional model of the airwaysis presented along with other elements that may be used by the clinicianduring treatment planning in step 36 of FIG. 3. The graphical userinterface may include display predictions about the patient's diseaseform, lung characteristic (such as the presence of collateralventilation, fissure integrity, etc.), outcome predictions, and/orprobability of adverse events as determined by quantitative CT analysis.An example of a graphical user interface is shown in the screenshotdepicted in FIG. 4. The screenshot 100 includes a 3-dimensional model ofthe patient's lungs 102 constructed by the system from the volumetricimaging data. In this example, the upper lobes are displayed in adifferent color (represented by light gray) and demonstrate how thedifferent lobes can be visualized. Alternatively, the sublobes may bedisplayed in different colors, for example. There is also a deviceselection window 104 and may also include a device diagram window (notshown). Once a device has been selected by the clinician, it may beidentified in the device selection window 104 and shown in the devicediagram window.

The user interface may also include predictions based upon analysis ofthe patient images using a prediction model as described here. In FIG.4, the user interface includes a prediction of the likelihood of asuccessful lung volume reduction 106, a prediction of collateralventilation 108, and a prediction of proper valve placement 110. Thevalues presented may be indicative of the percent likelihood and may beshown as a percent or a decimal fraction of one or less, or as any othervalue representing likelihood. Alternatively, the values may be shown aspresent/not present (absent). In another alternative, the values may bepresented as numerical scores indicating predicted amounts. For example,in the values shown in FIG. 4, the “CT score CV” of 0.1 represents a 10%likelihood of collateral ventilation. Alternatively or additionally, anyother types of prediction or scores could be shown, and alternativenomenclature or abbreviations may be used. For example, the display mayinclude predictions of the likelihood of success and/or of adverseevents for various treatment modalities, and or may display a preferredtreatment modality, based upon these or other predictions. Thepredictions provided by the system may be determined and displayed bythe system before a particular treatment and/or treatment location isselected by a clinician, such as to give a general indication of thelikelihood of success, for example. In some embodiments, the predictedfeatures determined in this step may be used to triage patients intothose who are eligible for particular procedures including endoscopiclung volume reduction procedures such as valve placement, coilplacement, energy delivery, or biosealant delivery, and those who arenot eligible. The results of this triage may be provided to theclinician on the display.

Next, the clinician may select a particular treatment modality in step38 and treatment location or treatment volume in step 40 of FIG. 2.Alternatively, the clinician may select a treatment modality earlier,such as between steps 32 and 34. One or more of the treatmentpredictions may vary depending upon the treatment modality, location, orvolume, such as the likelihood of lung volume reduction or likelihood ofa positive result. Therefore, after these selections are made, thepredicted features and outcomes may be determined for the first time, ormay be determined again now more specifically by taking into accountthese selections, and may be displayed for the clinician. That is, oneor more predictions may be calculated and displayed earlier, such asimmediately after analysis of the patient data in step 34. One or moreof these predictions may then be optimized and displayed again, and/orone or more other predictions may be displayed, after the user selectsthe treatment modality in step 38 and the treatment volume in step 40.

In step 42, the system may display the treatment plan which may includethe selected treatment modality, treatment volume, treatment location,and/or treatment pathway on the 3-dimensional airway model. The displaymay further include a display of predictions calculated by the systemusing predictors including predicted likelihood of successful outcomeand/or risk of adverse events (such valve placement errors,pneumothorax, and/or death) and the patient's volumetric images and insome cases the selected treatment plan. For example, the display mayinclude predicted features such as collateral ventilation and/orpredicted outcome such as likelihood of success and likelihood of anadverse event such as pneumothorax. The predictions provided in thisstep may be refined based on the treatment plan as compared topredictions that may have been provided in step 36. The plannedtreatment as displayed may be performed on the patient in accordancewith the plan.

An example of a display of a treatment plan is shown in FIG. 5, whichincludes a screenshot 100 with a 3-dimensional model of the patient'sairway to be treated 112 with an indication of the treatment locations114 and a display of the physical characteristics of the airways to betreated 116. The display further includes a device selection window 104in which an intrabronchial valve has been selected, and a display of theprobability of a valve placement error 120 and the probability of asuccessful lung volume reduction 122. This example further includesother elements which may be useful, including a 2 dimensional CT imageof the lungs and a virtual bronchoscopy image 126.

In addition to predicting treatment outcome, various embodiments may beused to predict features of the patient's lungs. With regard to theprediction of collateral ventilation, it is noted that such a predictionmay be considered as intra-lobar or inter-lobar collateral ventilation.Intra-lobar collateral ventilation may occur through the accessorypathways of the lungs including the intra-alveolar pores of Kohn, thebronchioalveolar communications of Lambert and the intrabronchiolarpathways of Martin. In certain conditions, such as emphysema, theseaccessory pathways can become enlarged and airway obstruction canincrease expiratory resistance, leading to the passage of air asintra-lobar collateral ventilation from one lobule to another.Interlobar collateral ventilation may occur when portions of theinterlobar fissures are absent or when the adjacent lobes become fusedto each other, resulting in an incomplete fissure and allowing aircommunication between the lobes at those locations.

Predictors of collateral ventilation may include the following:contralateral lung lower lobe tissue to air ratio, which is the tissueto air volume ratio in the lower lobe of the contralateral lung and maybe abbreviated CL_LL_TAR; fissure integrity for the fissure touching thetargeted lobe which may be abbreviated FI; contralateral lung lower lobeemphysema percent which may be abbreviated CL_LL_Emph and which may bemeasured as the percentage of emphysema below a threshold value, such asbelow −950 HU, −920HU, −910HU, or −856HU, for example; airway minimuminner diameter among the treated airways which may be abbreviatedMinInnerDiam and which is the smallest inner diameter of the plannedtreated airways; maximum tapering along the planned treated airwayswhich may be abbreviated MaxTapering; and minimum wall area percentageamong the treated airways (MinWAF), which is the area fraction that theairway wall occupies relative to the area described by the outer walland may be abbreviated MinWAF. Other predictors could also be identifiedby evaluation of the volumetric images in the training database. MinWAFis a unit-less value that lies in the range of 0 to 1. MaxTapering isthe maximum airway tapering among the treated airways in the targetedlobe and may be calculated as described further below. The fissureintegrity score can be computed as the percentage of completeness of thefissure. It provides a global quantitative assessment of possiblecollateral ventilation. With a fissure completeness of 100%, the fissureis intact and collateral ventilation between adjacent lobes is unlikely.In contrast, with a fissure completeness score of 0%, there is apossibility of collateral ventilation since no fissure serves as a sealbetween abutting lobes of the lung. The fissure integrity may beevaluated and scored as described in U.S. patent application Ser. No.13/804,542, the disclosure of which is hereby incorporated by reference.

Airway taper is an indication of whether an airway is getting larger orsmaller as the airway extends distally, with a normal airway ideallygrowing narrower as it proceeds distally. It may be calculated as:

$\frac{{{Inner}\mspace{14mu}{Lumen}\mspace{14mu}{Area}\mspace{14mu}\left( {40^{th}\mspace{14mu}{percentile}} \right)} - {{Inner}\mspace{14mu}{Lumen}\mspace{14mu}{Area}\mspace{14mu}\left( {70^{th}\mspace{14mu}{percentile}} \right)}}{{Inner}\mspace{14mu}{Lumen}\mspace{14mu}{Area}\mspace{14mu}\left( {40^{th}\mspace{14mu}{percentile}} \right)}$in which the percentile refers to the percentage of the centerlinelength, from the parent to the bifurcation to the next bifurcation, ofthe location at which the measurement is taken. Other methods ofcalculating airway taper are also possible and may alternatively beused. These predictors may be used alone or in combination to predictthe likelihood of collateral ventilation, and this may further be usedto determine whether a patient may be eligible for lung volume reductiontreatment. For example, in the basic schema shown in FIG. 6, the fissureintegrity score 130, the contralateral lower lobe emphysema score 132,and the collateral lower lobe tissue to air ratio 134 may be used aspredictors of lung volume reduction outcome 136 in a simple rule-basedclassifier to exclude candidates for lung volume reduction who arelikely to have collateral ventilation. Cut-off values may be selectedfor each of the parameters, such as those shown in FIG. 6. For example,in this classification system, if the fissure integrity score is greaterthan 80%, the contralateral lower lobe emphysema score is less than 40%,and the collateral lower lobe tissue to air ratio is less than 1.3, thepatient may be classified as not having collateral ventilation. In sucha case, lung volume reduction may be recommended. However, if thepatient fails to meet one of these thresholds, the patient may beclassified as having collateral ventilation and lung volume reductionusing valves may not be recommended. This model demonstrates how thepredictors may be used, but more sophisticated models may also be used,such as classification models in which the values of the predictorsthemselves (rather than the presence or absence of meeting a threshold)may be used in combination to classify a patient.

Another feature that may be predicted using predictors in a volumetricimage is the likelihood of problems with valve placement. Difficultieswith valve placement can have a significant impact on the outcome ofendobronchial lung volume reduction procedures. In some cases, theproblems could relate to procedural errors during valve placement. Inother cases the problems may be due to patient anatomy and/or devicefit, such as placement of a valve in an unintended location, such as ina branch distal to the intended location or in a bifurcation of anintended airway, or air leak may occur around a valve that allows thedistal airways to remain open. Predictors may be used to evaluate therisk of these types of valve placement problems. Examples of predictorsof valve placement problems include: centerline length, which is thecenterline length of airway branches in which the valve is to be placed;aspect ratio, which is the ratio of the centerline length to the averageinner airway area in the airway branch, and which gives insight into theshape of an airway; tapering, as described above; diameter of the targetlocation; and wall area fraction, which is the cross-sectional area ofthe airway wall relative to the total cross-sectional area of the airwayincluding the wall and the lumen. These predictors may be determined byextraction from the airway segmentation, for example. Other predictorscould also be identified by evaluation of the volumetric images in thetraining database. A schema to predict problems with lung valveplacement may be based on these parameters. The schema may be derivedaccording to the type of lung valve selected (such as endobronchial orintrabronchial). Several types of classifiers can be deployed using thepredictors, including rule based classifiers, tree-based classifiers,Naïve Bayes classifier, and more sophisticated classifiers.

In addition a likelihood of a valve placement with no misplacement,which may be abbreviated or indicated by the words Device Placement, canbe computed after an initial path of a treatment plan is determined. Itcan be re-computed after adjustment of the targeted airway locationuntil it is optimized.

Other features that may be predicted using predictors in the volumetricimages include the likelihood of adverse events and calculated lungcompliance. For example, lung compliance may be predicted using degreeof emphysema, air trapping and ventilation distribution derived fromregistering the inspiratory and expiratory scans of a given subject.Additional predictors may also be used. Alternatively, lung compliancecan be measured using a lung function study to determine residualvolume.

An alternative method of treatment outcome prediction and treatmentplanning is shown in the flow chart in FIG. 7. The method starts at step202, and then the system receives patient data at step 204, which may beany of the patient data as described previously and may include patientimages. In step 206 the system presents a graphical user interface tothe user, which may include options for types of procedure and types ofdevices for which prediction analysis will be conducted. An example ofsuch a graphical user interface is shown in FIG. 8. In this example, theuser may select between various options on the user interface 240including a lung cancer risk assessment box 242, a lung volume reductionoutcome prediction box 244 and a lung denervation outcome prediction box246. Other options for prediction may also be included, and further suboptions may be provided for selection by the user within each option.For example, within the option of lung volume reduction procedure, theuser may select one or more specific modalities. In the example shown inFIG. 8, these modalities for selection include a valve box 248, a vaporbox 250, and a coil box 252, though additional and/or alternativeoptions may also be provided. The user may select one or more desiredoptions for prediction by clicking the box on the graphical userinterface, after which the selected option or options are shown aschecked or otherwise indicated.

After a treatment modality and/or device are selected, the user or thesystem may then select a treatment location in step 208. For example,the user may select the treatment location by selecting a location on agraphical user interface including a 3-dimensional display of thepatient's lungs. Alternatively, the system may determine a best targetlocation for treatment using the patient data, and the selected locationmay be displayed for the user on a 3-dimensional display of thepatient's lungs. For example, the system may determine the besttreatment location based upon analysis of the patient's images, such asthe best location as determined based upon key measurements andpredictors such as percent emphysema, fissure integrity, and vesselmeasurements such as at various locations, such as in each lobe orsublobe, and/or across the lungs. In some embodiments, the system mayselect a treatment location which may be displayed on the userinterface, and the user may either accept the treatment location oroverride the selection and select a new treatment location using theuser interface. Once a treatment location is selected either by the useror by the system, the lobes selected for treatment may be highlighted,colored, or otherwise displayed in a manner to distinguish them from theuntreated lobes. An example of such a display is shown in FIG. 9, whichdepicts a graphical user interface 250 after a treatment location hasbeen selected. In this example, the lobe selected for treatment 252 isthe left upper lobe, which is displayed in a different color than theremainder of the lung. The graphical user interface further includesidentification of each lobe, a fissure integrity score 254 for fissureportions between pulmonary segments, the percentage of emphysema 256 inthe lobe to be treated, and the fissure integrity of the entire fissureof the lobe to be treated. Each of these may be automatically calculatedby the system and provided on the graphical user interface 250 alongwith the images of the lung 258.

Once a treatment location is selected, the system may calculate anddisplay outcome predictions in step 210. These predictions may varydepending upon the type of treatment modality or device. The predictionsmay be provided separately for each possible treatment modality ordevice selected by the user at step 206, for example, either on the samedisplay or on separate displays. Examples of displays of outcomeprediction are shown in FIGS. 10-12.

In FIG. 10, the display 260 includes a visual representation 262 of theprobability of a severe adverse event and the probability of asuccessful LVR outcome are shown on opposing x and y axis in the mannerof a graph. The display 260 shown is a summary, identified as an LVRSummary, and both selected treatment modality options, which in thisexample are valve and coil, are shown. The numerical values for theprobability of a severe adverse event 264 and of a successful LVRoutcome 266 are also shown for each selected treatment modality in thisexample. The user may select displays which show additional data foreach treatment modality separately, such as by clicking on the icons,which may be tabs as shown in this example.

FIGS. 11 and 12 present the visual displays of prediction for valveplacement and coil placement, respectively, at the selected location.The display includes a list of predictors as well as the results asdetermined by the system. In this example shown in FIG. 11, the visualdisplay 270 includes the predictors fissure integrity 272, LAC 274 (lowattenuation cluster slope which is the slope a of the log-log plot ofnumber of emphysematous holes versus hole size), and PPVV 276(percentage of peripheral vessel volume, which is the volume of the mostdistal vessels relative to the overall volume of all vessels) for valveplacement at the selected location. The main predictors were: fissureintegrity; percentage of peripheral vessel volume, which is the volumeof the most distal vessels relative to the overall volume of allvessels, and is abbreviated PPVV in table 1; low attenuation clusterslope which is the slope a of the log-log plot of number of(emphysematous) holes versus hole size and is abbreviated LAC in Table 1

In FIG. 12, the visual display 280 includes the predictors of percentemphysema 282, LAC-HSM 284, and LAC-HSSD 286, in which LAC-HSM is themean hole size of emphysema bullae and LAC-HSSD is the standarddeviation (and therefore an indication of the variability) of emphysemahole sizes. The displays may also include an image or representation ofthe lungs or part of the lungs. In FIG. 11, the visual display 270includes an image of the lung fissures 278, with the fissure shown intwo contrasting colors showing fissure that is present distinct fromfissure that is absent. In FIG. 12, the display 280 includes a visualrepresentation of the lungs 288 showing a representative depiction ofemphysema in different portions of the lungs. The emphysema depictionsare each provided in a different color in different lobes, and theamount of emphysema at a location is represented by the size of thespheres, with larger spheres representing a greater amount of emphysema.The images of pages to the sides of the lung figures may be selected bythe user to display other pertinent visual representations illustratingthe distribution of the main predictors that may be provided for reviewand browsing.

These predictions shown in FIGS. 10-12, for example, may be useful for auser for selecting which treatment modality to employ in a patient. In anext step 212 of FIG. 7 the treatment modality or device is selected,either by the system or by the user. The user may select the treatmentmodality or device by inputting the selection using a graphical userinterface and the system may receive the user's selection.Alternatively, the system may select the best treatment modality basedupon the outcome predictions determined for the treatment location. Theuser may have the option to override the selection made by the systemand select a different treatment modality or device and enter thatselection into the system using the user interface.

Once the treatment modality or device has been selected in step 212, thesystem and user may proceed with treatment planning. The system maypresent a treatment planning graphical user interface in step 214 andthe user may interact with the system to plan the treatment for thepatient as described previously above. The user may optimize deviceposition or adjust the treatment location to improve the procedureoutcome in step 216 and the system may update the outcome probabilitypredictions based upon the optimized device positioning in step 218. Forexample, the user may optimize the device position or treatment locationadjusting the treatment location, such as moving the device proximallyor distally in the same branch, or to a next proximal or distal branch,or to a new location, from that initially selected by the user orsystem. These updated outcome predictions may be used by the user inselecting the optimized treatment location and may be displayed for theuser on a graphical user interface as part of a final treatment plan.This optimized treatment plan may then be performed on the patient.

Another embodiment is shown in the flow chart displayed in FIG. 13. Inthis embodiment, the system may be used for predicting the risk of lungcancer and recommending lung cancer screening. In step 302, patient dataas described previously may be entered into the system which may includedata relevant to lung cancer risk, such as patient images, demographicssuch as age and gender, pulmonary function test results, and smokinghistory such as amount and duration of smoking (number of pack years).In step 304, the user may select, using a graphical user interface, theassessment to be performed by the system, which in this example is lungcancer risk assessment. An example of a user interface which may be usedfor this selection is the interface 310 shown in FIG. 14, in which theuser has selected lung cancer risk assessment box 312 to direct thesystem to perform lung cancer risk assessment. The system may thenanalyze the patient data to determine lung cancer risk, includinganalysis of the images using lung cancer predictors, and the results ofthe analysis may be displayed for the user on a new graphical userinterface, along with lung cancer screening recommendations in step 306.An example of such a graphical user interface is shown in FIG. 15, whichis a visual display 320 in which the patient demographics are shown,including age 322, number of pack-years the patient has smoked 324, andwhether or not the patient has smoked in the last 15 years 326. Otherdemographics may be included additionally or alternatively. Thegraphical user interface also displays various COPD assessments whichhave been determined by the system or input into the system, as patientdata, including Gold Status 328, mMRC 330, which stands for modifiedMedical Research Council Dyspnea scale, percent emphysema 332, andpercent air trapping 334. The graphical user interface in this examplefurther includes an image 336 which is a representation of the amount ofemphysema in the lungs as described previously. Finally, the systemdisplays whether lung cancer screening is recommended. In this example,the recommendation is a yes or no indication 338 based upon meeting theUSPSTF screening guidelines, but other guidelines may be used.Alternatively, lung cancer predictors may be used which were developedusing the databases as described herein. For example, the risk of lungcancer may be determined using demographic data and smoking history aswell as the presence of COPD and lung health evaluation based uponanalysis of the patient images and lung cancer risk models. In thisexample, the display further includes a yes or no indication 340 ofwhether or not COPD is present, and provides a numerical value of theamount of additional risk 342 due to the present of COPD, which in thisexample is 10%. The presence or absence of COPD may be determined,wholly or in part, using predictors as determined using the databases aspreviously described. In some embodiments, the display 320 may includespecific lung cancer screening recommendations, such as type ofscreening and frequency of screening recommended. Screening may then beperformed on the patient in accordance with the recommendation presentedon the display.

Example 1

Valve misplacements due to complex anatomy have been previously reportedbut little was known about anatomical factors linked to these proceduralissues. In this example, the anatomical properties leading to optimaldelivery of a valve to a targeted airway were investigated.

The CT scans of 184 subjects were retrospectively analyzed. The subjectshad severe emphysema and had undergone an LVR procedure following aunilateral complete occlusion protocol. The CT images included baselineimages (obtained prior to the procedure) and images obtained at 3-monthsfollow-up. The follow-up scans were used to assess the presence orabsence of valve placement problems. When valve placement problems wereidentified, they were classified as Type I or Type II. Procedural errorsdue to valves not being located in the targeted branch, i.e., beinglocated partially or entirely in a sub-segmental branch, were identifiedas Type I error. Lack of airway collapse distal to valve placement,either due to air leak around the valve or collateral ventilation, wasidentified as a Type II error.

Quantitative CT measurements of the treated airway segment wereextracted at 547 valve placement locations in baseline scans of thesubjects to provide insight into the shape and morphology of treatedairway branches. The measurements were: centerline length; average lumendiameter; circularity, wall thickness (WT), wall area fraction (WAF)measured as the fraction that the area of the airway wall adjacent tothe valve relative to the area of the entire airway branch in which thevalve was placed; tapering, measured as the ratio of inner airway areaof the distal end relative to the proximal end of the airway branch; andthe aspect ratio, measured as the ratio of centerline length to theaverage inner airway area. All quantitative CT measurements wereautomatically computed using Apollo software (VIDA Diagnostics).

The seven airway measurements as well as data including the valve type(endobronchial or intrabronchial), the treated lobe, and the presence orabsence of valve placement errors were used to feed a logisticregression analysis to determine quantitative CT predictors of valveplacement errors. A generalized estimating equations (GEE) methodaccounted for correlation between measurements. Type I errors werepresent in 19.6% (107/547), Type II errors were present in 38.2%(209/547), and in 42.2% (231/547) there were no valve placement errors.Valve type and lobe did not have an effect on the occurrence of valveplacement errors. However, three predictors were identified assignificant by regression analysis: wall thickness (p=0.0097); wall areafraction (p=0.01 when 0.62<WAF≤0.65 vs. WAF>0.65; p<0.001 when WAF<=0.62vs. WAF>0.65); and tapering (p<0.001 when tapering≤0.13 vs.tapering>0.13). Smaller wall thicknesses and greater wall area fractionand tapering are associated with lower probability of valve problems,which is typical of smaller airways.

The results of this example are shown graphically in FIGS. 16-18. FIG.16 is a graph of wall thickness verses the probability of a Type I orType II valve errors. Likewise the probability wall area fraction andtapering and valve errors are shown versus in FIGS. 17 and 18,respectively. In each case, the solid line represents the calculatedprobability and the dashed lines represent the confidence interval.

This example shows that quantitative CT analysis can help identifyairways for optimal anatomic fit for valves, thereby reducing theprobability of procedural issues and providing an objective and moreefficient approach to treatment planning.

Example 2

In this example, the relationship between lung volume reduction and theoccurrence of pneumothorax was retrospectively analyzed using CT images.CT scans of 183 subjects with severe emphysema were analyzed. Thesubjects had all undergone lung volume reduction with valves werefollowing a unilateral complete occlusion protocol. The CT scans wereacquired at Total Lung Capacity (TLC) and included a baseline scan and afollow up scan at 3 months after the procedure. The volumes of treatedlobes were measured on the baseline and 3-month follow-up CT scans.Post-procedural pneumothorax events were identified and valve placementerrors were studied retrospectively by analyzing follow-up CT images.Thirty-five baseline variables including confounding variables (personalcharacteristics as described above), CT quantitative measurement offissure integrity (FI), density, and vessel measurements were used tofeed a logistic regression analysis in order to find significantpredictors of lung volume reduction outcome. Quantitative CTmeasurements were extracted from the baseline CT scans and computedusing dedicated lung Quantitative Imaging software (Apollo®, VIDADiagnostics, Coralville, Iowa). Lobar volumes were also measured on bothbaseline and follow-up scans. A lobar volume reduction greater than 350cc was used to identify a positive response to the lung volume reductionprocedure.

Of the total group, 12% (22/183) experienced one pneumothorax event,with the following lobar distribution: left lower lobe (n=12), leftupper lobe (n=6), right lower lobe (n=3), and right upper lobe (n=1). Apredominance of pneumothorax events occurred in the left lung and in thelower lobes, in particular the left lower lobe (p=0.02).

A regression analysis was performed on the data to identify the mainquantitative CT predictors of pneumothorax following lung volumereduction procedures. The identified predictors were the fissureintegrity of the treated lobe (p=0.01) and the volume of the treatedlobe (p=0.01). Hyper-inflated lobes and complete fissures commonlyassociated with successful lung volume reduction procedures were thusassociated with a higher probability of pneumothorax. A graph showingthe trade-off between successful lung volume reduction and theoccurrence of pneumothorax is shown in FIG. 19. The modeling of the lungvolume reduction/pneumothorax trade-off in FIG. 19 shows a steadyincrease of pneumothorax events when the probability of a successfullung volume reduction procedure exceeds 50%. When there were no valveplacement errors, the probability of a pneumothorax event wassignificantly higher (probability of 16%) than when one or few valveplacement errors occurred (probability of 6%) impairing the lung volumereduction procedure results.

The probability of a pneumothorax event in an endobronchial valve lungvolume reduction procedure following a unilateral partial occlusionprotocol was found to be inversely related to a successful lung volumereduction (with a lung volume reduction greater than 350 cc).Quantitative CT can be used to measure predictors of pneumothorax andsuccessful lung volume reduction to determine the risk of pneumothoraxand whether a lung volume reduction procedure is likely to be effectivefor a patient in clinical practice, as well as to evaluate the tradeoffbetween the risks and benefit of the procedure.

Example 3

This example was performed to evaluate the relationship between fissureintegrity and lung volume reduction procedure outcome. CT scans of 183subjects with severe emphysema were analyzed as in Example 2. Thesubjects had all undergone lung volume reduction with valve placementfollowing a unilateral complete occlusion protocol using eitherendobronchial valves (Zephyr™ valves from Pulmonx Inc.; n=37) orintrabronchial valves (IBV® valves from Olympus Medical Co.; n=146). TheCT scans were acquired at full inspiration and included a baseline scanprior to the procedure and a follow up scan at 3 months after theprocedure. Volumes of treated lobes were measured on baseline and3-month follow-up CT scans, and a lobar volume reduction greater than350 cc was considered to be indicative of positive response totreatment. Thirty-five baseline variables including confoundingvariables (personal characteristics as described above), CT quantitativemeasurement of fissure integrity (FI), density, and vessel measurementswere used to feed a logistic regression analysis in order to findsignificant predictors of lung volume reduction outcome, as in Example2. All the quantitative CT measurements were automatically computed fromthe CT scans using dedicated lung Quantitative Imaging software (Apollo,VIDA Diagnostics), with access to lobe editing for precise density,vessel and fissure integrity measurements.

After elimination of highly correlated variables and step-wiseregression analysis, the main quantitative CT predictors which werecorrelated to successful lung volume reduction are shown in Table 1below. The main predictors were: fissure integrity; percentage ofperipheral vessel volume, which is the volume of the most distal(segmented) vessels relative to the overall volume of all (segmented)vessels and is abbreviated PPVV in Table 1; low attenuation clusterslope which is the slope a of the log-log plot of number of(emphysematous) holes versus hole size and is abbreviated LAC in Table1; valve type as endobronchial or intrabronchial; number of valves usedin the treated lobe; and tissue-to-air volume ratio in the lower lobe ofthe treated lung, abbreviated TreatedLL_TAR in Table 1. A description ofthe calculation of low attenuation cluster slope can be found in MishimaM, Hirai T, Itoh H, Nakano Y, Sakai H, Muro S, Nishimura K, Oku Y, ChinK, Ohi M, Nakamura T, Bates J H, Alencar A M, Suki B (1999) Complexityof terminal airspace geometry assessed by lung computed tomography innormal subjects and patients with chronic obstructive pulmonary disease.Proc Natl Acad Sci USA 96(16):8829-8834, for example. The whole lung wasused for the calculation of the percentage of peripheral vessel volumein this example, but more localized measurements of PPVV could be used,such as by including only the lobar or sub-lobar vessels in thecalculation. The cutoff between peripheral and central lung and vesselareas, such as for use in calculating PPVV, may be determined in avariety of ways. In this example, one iteration of a morphologicalerosion was applied to the vascular segmentation mask of the entireairway tree. This erosion removed a layer of most distal vascularvoxels, which were then defined as peripheral. Alternatively, allvessels which are situated in the periphery of the lung at a certaindistance from the pleura, such as within 3 cm, may be classified asperipheral while the remainder may be classified as central. In stillother embodiments, one may define vessels as peripheral if they are thelast vessel generations. Other measures of vascular destruction due toemphysematous or other lung disease besides PPVV, such as peripheral andartery-specific vascular measures, may alternatively be used as they canprovide complementary value to parenchymal density measures and improvethe predictive abilities of the system.

TABLE 1 Odds Ratio Predictors P-value (95% CI) FI <.0001 per +5:1.39(1.20, 1.61) PPVV <0.53 vs. ≥0.53 0.007 2.87 (1.33, 6.18) LAC <−1.32 vs.≥−1.32 0.058 1.97 (0.98, 3.96) Valve type EBV vs. IBV 0.01 3.25 (1.32,7.99) Number of 4-6 vs. 1-2 0.04 2.80 valves (1.02, 7.67) TreatedLL_TAR<0.125 vs. ≥0.125 0.048 2.25 (1.01, 5.03)

There was a difference in the rate of successful treatment betweensubjects with intrabronchial valves (40.5%) compared to endobronchialvalves (56.2%) which skewed the results. When patients treated withintrabronchial valves were excluded and only those treated withendobronchial valves were analyzed, the several predictors shown inTable 1 above became insignificant. These predictors were valve type,number of valves, and tissue-to-air volume ratio in the lower lobe ofthe treated lung. Fissure integrity (p<0.0001), low attenuation clusterslope (p=0.01), and peripheral vessel volume (p=0.02) were identified asthe main predictors of successful lung volume reduction forendobronchial valves. The odds ratio for positive lung volume reductionincreased by 1.47 times for every 5% increase in fissure integrity(CI=[1.25, 1.72]), by 2.68 times when low attenuation cluster slope wasless than −1.32 (CI=[1.21, 5.92]), and by 2.72 times when the peripheralvessel volume was less than 0.53 (CI=[1.14, 6.50]). ROC curves (AUC)demonstrated the superiority of the full quantitative CT model using thethree predictors (fissure integrity, low attenuation cluster slope, andperipheral vessel volume, with an AUC=0.80) as compared to a 1-predictormodel using fissure integrity alone (AUC=0.75).

QCT has the potential to improve prediction of successful lung volumereduction using CT-measured surrogates for collateral ventilation and anew vascular index of disease distribution, percentage of peripheralvessel volume or PPVV, introducing an objective and more efficientapproach to patient selection and treatment planning.

Example 4

The Chartis Pulmonary Assessment System™ has a reported accuracy levelof 75% in predicting patient response to valve-based lung volumereduction therapy, with the definition of success being a 350 ccreduction in volume of the treated lobe following the procedure. In thisexample, CT predictors of response to endobronchial valve lung reductiontherapy identified in Example 3 above were retrospectively compared tothe use of the Chartis Pulmonary Assessment System for selection ofpatients likely to have successful outcome from a valve-based lungvolume reduction procedure.

Pre-operative CT scans of 146 subjects who underwent endobronchial valveLVR with EBV valves (Zephyr™ EBV; Pulmonx Inc.) following a unilateralcomplete occlusion protocol. CT scans had been obtained at a baselineand at 3 months following the procedure. The scans were analyzedretrospectively using dedicated lung Quantitative Imaging software(Apollo®, VIDA Diagnostics, Iowa). Volumes of treated lobes weremeasured on the baseline and follow-up CT scans of each subject. A lobarvolume reduction greater than 350 cc was considered to be indicative ofpositive response to treatment. The quantitative CT measures identifiedabove in Example 3 as correlating to a successful lung volume reductionprocedure for endobronchial valve placement were measured in thesubjects' scans. These were: fissure integrity, low attenuationclusters, and patient's percent peripheral vessel volume. We refer tothe use of these three predictors as the “full quantitative CT model.”

First, we analyzed the ROC of the full quantitative CT model versus amodel using fissure integrity only, with fissure integrity determinedusing the CT images as described above, using the full dataset topredict successful lung volume reduction treatment. The ROC analysisconducted on the full dataset validated the superiority of the fullquantitative CT model (AUC=0.80) over the model consisting of using FIalone (the univariate model) to determine likelihood of successful LVRresponse (AUC=0.75). At the operating point corresponding to FI≥90% inthe univariate model, the sensitivity was 76.8% and specificity was60.9%. For the full quantitative CT model, the same specificity wasachieved as the univariate model, but with a sensitivity of 90.2%. Usingthe univariate model, a fissure integrity of 82.9% corresponded to a 50%probability of successful lung volume reduction. With the full QCTmodel, the same probability of successful lung volume reduction (50%)could be obtained with a range of FI values from 72.9% to 98.9%,depending on the other variables, that is the low attenuation clusters,and patient's percent peripheral vessel volume.

Next, a subset of the subjects (n=113) without Chartis data were used totrain the quantitative CT-Bayes classifier to maximize successful lungvolume reduction predictive score. The remaining subjects (n=33) forwhom the data set included data from a Chartis Pulmonary AssessmentSystem,™ were then used as testing datasets to evaluate the relativeperformance of the quantitative CT-Bayes classifier versus Chartis inselecting those likely to have a successful outcome from lung volumereduction therapy. Table 2, below, confirms that QCT-Bayespatient-selection method is superior to univariate quantitative CTmodule using fissure integrity alone and is comparable to the predictionof successful lung volume reduction using the Chartis PulmonaryAssessment System.™

TABLE 2 Patient # Patients Selection Recommended Responder Sensi- Speci-Method for treatment Rate Accuracy tivity ficity Chartis 57.6% 78.9%78.8% 83.3% 73.3% (19/33) (15/19) (26/33) (15/18) (11/15) QCT-Bayes63.6% 76.2% 78.8% 88.9% 66.7% (21/33) (16/21) (26/33) (16/18) (10/15)QCT-FI ≥ 57.6% 73.7% 72.7% 77.8% 66.7% 90% (19/33) (14/19) (24/33)(14/18) (10/15)

Example 5

Bronchoscopy-guided lung volume reduction treatment with coils is a newprocedure targeting the treatment of patients with severe emphysema. Ithas been shown to be effective in treating both homogeneous andheterogeneous emphysema patients, with and without indicators ofcollateral ventilation. The purpose of this Example is to identifypre-operative Computed Tomography (CT) quantitative measurementsassociated with positive treatment outcome.

Computed tomography scans from 22 subjects with severe emphysemapatients were acquired at full inspiration prior to the procedure. Lungvolume reduction coils (PneumRx, Mountain View, Calif.) were thenimplanted to one targeted lobe using fluoroscopic guidance. Clinicaldata including FEV1, residual volume (RV) and 6-minute walk distance(6MWD) were collected prior to the procedure and at 3 months aftertreatment. Changes in these clinical data were used as indicators of thetreatment efficacy. Quantitative CT measurements were determined fromthe pre-operative scans using dedicated lung quantitative imagingsoftware Apollo® (VIDA Diagnostics, Coralville, Iowa). Thesemeasurements were for the targeted lobe and included: fissure integrity;emphysema percentage, abbreviated EP, which was defined as percentage oflow attenuation areas below a threshold of −950 HU; and heterogeneityscore, abbreviated HS, which was defined as the difference betweenpercentage between the treated lobe and the non-treated ipsilateral lobeor the weighted average of the ipsilateral non-treated lobes. Inaddition, the emphysema severity and subtype was determined in thetargeted lobe by performing low attenuation cluster (LAC) basedmeasurements (slope, hole size mean (HSM) which is the mean volume of alow attenuation cluster (contiguous voxels of below a threshold of−950HU), and hole size standard deviation (HSSD) which is the standarddeviation of the size of the low attenuation clusters). A threshold of−950 Hounsfield Unit (HU) was used in the analysis as a CT indicator ofemphysema. Spearman correlations between efficacy measurements and CTderived quantitative measurements were evaluated.

Among the subjects, 68.2% (15/22) experienced a large improvement inexercise capacity with a change in 6 minute walking distance of greaterthan or equal to 26 meters. The difference in emphysema percentagebetween those identified as having a successful procedure result ascompared to those EP difference was shown to be significant (p=0.04) butthe significant difference was observed for fissure integrity (p=0.19)or HS (p=0.95). As shown in Table 3, below, HS and FI had a weak or nocorrelation against all efficacy measurements, which suggests thatinter-lobar collateral ventilation and emphysema heterogeneity acrossthe treated lung may not play important roles in coil-based lung volumereduction procedures. However, significant correlations were observedfor the following intra-lobar quantitative CT measurements: EP vs. Δ6MWD(ρ=0.59), EP vs. ΔRV (ρ=−0.72), LAC-HSM vs. ΔRV (ρ=−0.63), LAC-HSM vs.Δ6MWD (ρ=−0.49), LAC-HSSD vs. ΔRV (ρ=−0.69), and LAC-HSSD vs. Δ6MWD(ρ=−0.56). No significant correlation was observed between ΔFEV1 andquantitative measurements.

TABLE 3 ΔFEV1 ΔRV Δ6MWD FI −0.043 (0.85)  0.13 (0.56) 0.17 (0.46)  EP0.29 (0.19)  −0.72 (<0.001) 0.59 (<0.01) HS −0.027 (0.91)  −0.30 (0.17) 0.06 (0.79)  LAC-Slope 0.18 (0.41) −0.44 (<0.05) 0.26 (0.24)  LAC- HSM0.19 (0.39) −0.63 (<0.01) 0.49 (<0.05) LAC-HSSD  0.2 (0.37)  −0.69(<0.001) 0.56 (<0.01)

These results suggest that quantitative CT measurements characterizingemphysema magnitude, distribution and severity within the treated lobeare associated with the efficacy of lung volume reduction coilprocedures. These quantitative CT measurements may therefore be used aspredictors for improved patient selection to achieve a greater treatmentresponse.

Example 6

In this example, 385 CT scans were analyzed using dedicated quantitativeCT lung software as described herein. The subjects of the scans werecurrent and former smokers, both with and without lung disease. Theanalysis included QCT measurements of density, airway measurements,vessel measurements, functional measurements, and nodule dimensions andcharacteristics.

Demographic information was available for 380 of the 385 subjects. Ofthese, 311 were 55 years old or older, 228 had a 30 or more pack-yearhistory of smoking, 344 had less than 15 years since quitting smoking,and 120 had proven lung cancer by biopsy or imaging. There were equalnumbers of women and men.

The analysis showed that air trapping and airway thickness were bothstrongly associated with lung cancer, with each having a p<0.01.Parenchymal measurements including low attenuation clustersrepresentative of disease severity (p=0.01) and a centrilobular patternof emphysema as assessed by visual experts (p=0.04) were also associatedwith lung cancer but less strongly than the airway disease related QCTmetrics.

These results show that QCT measurement can be used to predict increasedrisk of lung cancer and can be applied to a system for recommending lungcancer screening. The prediction of lung cancer risk can be improvedthrough the use of functional airway and emphysema measurements as wellas QCT measurements of longitudinal differences.

In the foregoing detailed description, the invention has been describedwith reference to specific embodiments. However, it may be appreciatedthat various modifications and changes can be made without departingfrom the scope of the invention.

The invention claimed is:
 1. A system for predicting an outcome of alung procedure for a patient comprising: a processor; programingoperable on the processor for predicting the outcome of the lungprocedure; wherein predicting the outcome of the lung procedurecomprises: receiving patient data comprising volumetric images of thepatient; analyzing the volumetric images to identify one or morefeatures correlated to treatment outcome prediction, wherein the one ormore identified features comprises at least one measurement related tolung parenchyma, airways in the lung, pulmonary vessels, and/or one ormore lobar fissures; predicting an outcome for a treatment modality ortreatment device using the one or more identified features; andgenerating data representing text and/or imagery of the predictedoutcome.
 2. The system of claim 1, wherein receiving patient datafurther comprises at least one measurement not directly related to thevolumetric images.
 3. The system of claim 2, wherein the at least onemeasurement not directly related to the volumetric images comprises atleast one personal characteristics of the patient from a list consistingof: age, gender, race, body mass index, height, and lung volume.
 4. Thesystem of claim 1, wherein predicting the outcome for the treatmentmodality or treatment device includes comparing the one or moreidentified features to a database to predict an outcome of treatment atthe selected location with the selected treatment modality.
 5. Thesystem of claim 1, further comprising a user interface.
 6. The system ofclaim 5, wherein predicting the outcome of the lung procedure furthercomprises displaying the predicted outcome on the user interface.
 7. Thesystem of claim 1, wherein the lung procedure comprises a lung volumereduction procedure.
 8. The system of claim 6 wherein displaying thepredicted outcome of the lung procedure further comprises receiving aselected treatment modality from a user, and wherein predicting anoutcome for a treatment modality comprises predicting an outcome for thetreatment modality selected by the user.
 9. The system of claim 6wherein predicting an outcome for a treatment modality comprisespredicting a plurality of outcomes for a plurality of treatmentmodalities, and wherein displaying the predicted outcome on the userinterface comprises displaying the plurality of treatment outcomes forthe plurality of treatment modalities on the user interface.
 10. Asystem for predicting an outcome of a lung procedure for a patientcomprising: a processor; programing operable on the processor forpredicting the outcome of the lung procedure; wherein predicting theoutcome of the lung procedure comprises: receiving patient datacomprising volumetric images of the patient; analyzing the volumetricimages to identify lobes and airway tree of the lungs; analyzing thevolumetric images to identify one or more features correlated totreatment outcome prediction, wherein the one or more identifiedfeatures comprises at least one measurement related to lung parenchyma,airways in the lung, pulmonary vessels, and/or one or more lobarfissures; receiving a selected treatment location within the airwaytree; predicting an outcome for a treatment modality or treatment deviceat the selected location using the one or more identified features; andgenerating data representing text and/or imagery of the predictedoutcome.
 11. The system of claim 10 wherein displaying the predictedoutcome of the lung procedure further comprises receiving a selectedtreatment modality or treatment device from a user, and whereinpredicting an outcome for a treatment modality comprises predicting anoutcome for the treatment modality or treatment device selected by theuser.
 12. The system of claim 10 wherein the predicted outcome comprisesa numerical value representing a probability.
 13. The system of claim 10wherein predicting an outcome for the selected treatment modality usingone or more identified features comprises comparing the one or morefeatures identified in the patient lungs to a database to predict anoutcome of treatment at the selected location with the selectedtreatment modality.
 14. The system of claim 13 wherein the databasecomprises a set of outcomes for lung procedures for a group ofindividuals using the selected treatment modality and further comprisesa set of volumetric images or one or more features identified in thevolumetric images for the group of individuals, wherein the identifiedfeatures in the volumetric images of the group of individuals are thesame features as the identified features in the volumetric images of thepatient.
 15. The system of claim 10, wherein receiving patient datafurther comprises at least one measurement not directly related to thevolumetric images.
 16. The system of claim 15, wherein the at least onemeasurement not directly related to the volumetric images comprises atleast one personal characteristics of the patient from a list consistingof: age, gender, race, body mass index, height, and lung volume.
 17. Thesystem of claim 10, further comprising a user interface.
 18. The systemof claim 17, wherein receiving the selected treatment location withinthe airway tree comprises receiving the selected treatment location froma user via the user interface.
 19. The system of claim 17, whereinpredicting the outcome of the lung procedure further comprisesdisplaying the predicted outcome on the user interface.
 20. The systemof claim 10, wherein the lung procedure comprises a lung volumereduction procedure.